ml.hpp___openCV的自带函数源文件
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#ifndef OPENCV_ML_HPP
#define OPENCV_ML_HPP
#ifdef __cplusplus
# include "opencv2/core.hpp"
#endif
#ifdef __cplusplus
#include & data, bool test, OutputArray resp ) const;
/** @brief Predicts response(s) for the provided sample(s)
@param samples The input samples, floating-point matrix
@param results The optional output matrix of results.
@param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags.
*/
CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
/** @brief Create and train model with default parameters
The class must implement static `create()` method with no parameters or with all default parameter values
*/
template static Ptr<_Tp> train(const Ptr& data, int flags=0)
{
Ptr<_Tp> model = _Tp::create();
return !model.empty() && model->train(data, flags) ? model : Ptr<_Tp>();
}
};
/****************************************************************************************\
* Normal Bayes Classifier *
\****************************************************************************************/
/** @brief Bayes classifier for normally distributed data.
@sa @ref ml_intro_bayes
*/
class CV_EXPORTS_W NormalBayesClassifier : public StatModel
{
public:
/** @brief Predicts the response for sample(s).
The method estimates the most probable classes for input vectors. Input vectors (one or more)
are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one
output vector outputs. The predicted class for a single input vector is returned by the method.
The vector outputProbs contains the output probabilities corresponding to each element of
result.
*/
CV_WRAP virtual float predictProb( InputArray inputs, OutputArray outputs,
OutputArray outputProbs, int flags=0 ) const = 0;
/** Creates empty model
Use StatModel::train to train the model after creation. */
CV_WRAP static Ptr create();
/** @brief Loads and creates a serialized NormalBayesClassifier from a file
*
* Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk.
* Load the NormalBayesClassifier from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized NormalBayesClassifier
* @param nodeName name of node containing the classifier
*/
CV_WRAP static Ptr load(const String& filepath , const String& nodeName = String());
};
/****************************************************************************************\
* K-Nearest Neighbour Classifier *
\****************************************************************************************/
/** @brief The class implements K-Nearest Neighbors model
@sa @ref ml_intro_knn
*/
class CV_EXPORTS_W KNearest : public StatModel
{
public:
/** Default number of neighbors to use in predict method. */
/** @see setDefaultK */
CV_WRAP virtual int getDefaultK() const = 0;
/** @copybrief getDefaultK @see getDefaultK */
CV_WRAP virtual void setDefaultK(int val) = 0;
/** Whether classification or regression model should be trained. */
/** @see setIsClassifier */
CV_WRAP virtual bool getIsClassifier() const = 0;
/** @copybrief getIsClassifier @see getIsClassifier */
CV_WRAP virtual void setIsClassifier(bool val) = 0;
/** Parameter for KDTree implementation. */
/** @see setEmax */
CV_WRAP virtual int getEmax() const = 0;
/** @copybrief getEmax @see getEmax */
CV_WRAP virtual void setEmax(int val) = 0;
/** %Algorithm type, one of KNearest::Types. */
/** @see setAlgorithmType */
CV_WRAP virtual int getAlgorithmType() const = 0;
/** @copybrief getAlgorithmType @see getAlgorithmType */
CV_WRAP virtual void setAlgorithmType(int val) = 0;
/** @brief Finds the neighbors and predicts responses for input vectors.
@param samples Input samples stored by rows. It is a single-precision floating-point matrix of
`<number_of_samples> * k` size.
@param k Number of used nearest neighbors. Should be greater than 1.
@param results Vector with results of prediction (regression or classification) for each input
sample. It is a single-precision floating-point vector with `<number_of_samples>` elements.
@param neighborResponses Optional output values for corresponding neighbors. It is a single-
precision floating-point matrix of `<number_of_samples> * k` size.
@param dist Optional output distances from the input vectors to the corresponding neighbors. It
is a single-precision floating-point matrix of `<number_of_samples> * k` size.
For each input vector (a row of the matrix samples), the method finds the k nearest neighbors.
In case of regression, the predicted result is a mean value of the particular vector' s neighbor
responses. In case of classification, the class is determined by voting.
For each input vector, the neighbors are sorted by their distances to the vector.
In case of C++ interface you can use output pointers to empty matrices and the function will
allocate memory itself.
If only a single input vector is passed, all output matrices are optional and the predicted
value is returned by the method.
The function is parallelized with the TBB library.
*/
CV_WRAP virtual float findNearest( InputArray samples, int k,
OutputArray results,
OutputArray neighborResponses=noArray(),
OutputArray dist=noArray() ) const = 0;
/** @brief Implementations of KNearest algorithm
*/
enum Types
{
BRUTE_FORCE=1,
KDTREE=2
};
/** @brief Creates the empty model
The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
*/
CV_WRAP static Ptr<KNearest> create();
/** @brief Loads and creates a serialized knearest from a file
*
* Use KNearest::save to serialize and store an KNearest to disk.
* Load the KNearest from this file again, by calling this function with the path to the file.
*
* @param filepath path to serialized KNearest
*/
CV_WRAP static Ptr<KNearest> load(const String& filepath);
};
/****************************************************************************************\
* Support Vector Machines *
\****************************************************************************************/
/** @brief Support Vector Machines.
@sa @ref ml_intro_svm
*/
class CV_EXPORTS_W SVM : public StatModel
{
public:
class CV_EXPORTS Kernel : public Algorithm
{
public:
virtual int getType() const = 0;
virtual void calc( int vcount, int n, const float* vecs, const float* another, float* results ) = 0;
};
/** Type of a %SVM formulation.
See SVM::Types. Default value is SVM::C_SVC. */
/** @see setType */
CV_WRAP virtual int getType() const = 0;
/** @copybrief getType @see getType */
CV_WRAP virtual void setType(int val) = 0;
/** Parameter \f$\gamma\f$ of a kernel function.
For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */
/** @see setGamma */
CV_WRAP virtual double getGamma() const = 0;
/** @copybrief getGamma @see getGamma */
CV_WRAP virtual void setGamma(double val) = 0;
/** Parameter _coef0_ of a kernel function.
For SVM::POLY or SVM::SIGMOID. Default value is 0.*/
/** @see setCoef0 */
CV_WRAP virtual double getCoef0() const = 0;
/** @copybrief getCoef0 @see getCoef0 */
CV_WRAP virtual void setCoef0(double val) = 0;
/** Parameter _degree_ of a kernel function.
For SVM::POLY. Default value is 0. */
/** @see setDegree */
CV_WRAP virtual double getDegree() const = 0;
/** @copybrief getDegree @see getDegree */
CV_WRAP virtual void setDegree(double val) = 0;
/** Parameter _C_ of a %SVM optimization problem.
For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */
/** @see setC */
CV_WRAP virtual double getC() const = 0;
/** @copybrief getC @see getC */
CV_WRAP virtual void setC(double val) = 0;
/** Parameter \f$\nu\f$ of a %SVM optimization problem.
For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */
/** @see setNu */
CV_WRAP virtual double getNu() const = 0;
/** @copybrief getNu @see getNu */
CV_WRAP virtual void setNu(double val) = 0;
/** Parameter \f$\epsilon\f$ of a %SVM optimization problem.
For SVM::EPS_SVR. Default value is 0. */
/** @see setP */
CV_WRAP virtual double getP() const = 0;
/** @copybrief getP @see getP */
CV_WRAP virtual void setP(double val) = 0;
/** Optional weights in the SVM::C_SVC problem, assigned to particular classes.
They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus
these weights affect the misclassification penalty for different classes. The larger weight,
the larger penalty on misclassification of data from the corresponding class. Default value is
empty Mat. */
/** @see setClassWeights */
CV_WRAP virtual cv::Mat getClassWeights() const = 0;
/** @copybrief getClassWeights @see getClassWeights */
CV_WRAP virtual void setClassWeights(const cv::Mat &val) = 0;
/** Termination criteria of the iterative %SVM training procedure which solves a partial
case of constrained quadratic optimization problem.
You can specify tolerance and/or the maximum number of iterations. Default value is
`TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`; */
/** @see setTermCriteria */
CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0;
/** @copybrief getTermCriteria @see getTermCriteria */
CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
/** Type of a %SVM kernel.
See SVM::KernelTypes. Default value is SVM::RBF. */
CV_WRAP virtual int getKernelType() const = 0;
/** Initialize with one of predefined kernels.
See SVM::KernelTypes. */
CV_WRAP virtual void setKernel(int kernelType) = 0;
/** Initialize with custom kernel.
See SVM::Kernel class for implementation details */
virtual void setCustomKernel(const Ptr<Kernel> &_kernel) = 0;
//! %SVM type
enum Types {
/** C-Support Vector Classification. n-class classification (n \f$\geq\f$ 2), allows
imperfect separation of classes with penalty multiplier C for outliers. */
C_SVC=100,
/** \f$\nu\f$-Support Vector Classification. n-class classification with possible
imperfect separation. Parameter \f$\nu\f$ (in the range 0..1, the larger the value, the smoother
the decision boundary) is used instead of C. */
NU_SVC=101,
/** Distribution Estimation (One-class %SVM). All the training data are from
the same class, %SVM builds a boundary that separates the class from the rest of the feature
space. */
ONE_CLASS=102,
/** \f$\epsilon\f$-Support Vector Regression. The distance between feature vectors
from the training set and the fitting hyper-plane must be less than p. For outliers the
penalty multiplier C is used. */
EPS_SVR=103,
/** \f$\nu\f$-Support Vector Regression. \f$\nu\f$ is used instead of p.
See @cite LibSVM for details. */
NU_SVR=104
};
/** @brief %SVM kernel type
A comparison of different kernels on the following 2D test case with four classes. Four
SVM::C_SVC SVMs have been trained (one against rest) with auto_train. Evaluation on three
different kernels (SVM::CHI2, SVM::INTER, SVM::RBF). The color depicts the class with max score.
Bright means max-score \> 0, dark means max-score \< 0.
*/
enum KernelTypes {
/** Returned by SVM::getKernelType in case when custom kernel has been set */
CUSTOM=-1,
/** Linear kernel. No mapping is done, linear discrimination (or regression) is
done in the original feature space. It is the fastest option. \f$K(x_i, x_j) = x_i^T x_j\f$. */
LINEAR=0,
/** Polynomial kernel:
\f$K(x_i, x_j) = (\gamma x_i^T x_j + coef0)^{degree}, \gamma > 0\f$. */
POLY=1,
/** Radial basis function (RBF), a good choice in most cases.
\f$K(x_i, x_j) = e^{-\gamma ||x_i - x_j||^2}, \gamma > 0\f$. */
RBF=2,
/** Sigmoid kernel: \f$K(x_i, x_j) = \tanh(\gamma x_i^T x_j + coef0)\f$. */
SIGMOID=3,
/** Exponential Chi2 kernel, similar to the RBF kernel:
\f$K(x_i, x_j) = e^{-\gamma \chi^2(x_i,x_j)}, \chi^2(x_i,x_j) = (x_i-x_j)^2/(x_i+x_j), \gamma > 0\f$. */
CHI2=4,
/** Histogram intersection kernel. A fast kernel. \f$K(x_i, x_j) = min(x_i,x_j)\f$. */
INTER=5
};
//! %SVM params type
enum ParamTypes {
C=0,
GAMMA=1,
P=2,
NU=3,
COEF=4,
DEGREE=5
};
/** @brief Trains an %SVM with optimal parameters.
@param data the training data that can be constructed using TrainData::create or
TrainData::loadFromCSV.
@param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is
executed kFold times.
@param Cgrid grid for C
@param gammaGrid grid for gamma
@param pGrid grid for p
@param nuGrid grid for nu
@param coeffGrid grid for coeff
@param degreeGrid grid for degree
@param balanced If true and the problem is 2-class classification then the method creates more
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
If there is no need to optimize a parameter, the corresponding grid step should be set to any
value less than or equal to 1. For example, to avoid optimization in gamma, set `gammaGrid.step
= 0`, `gammaGrid.minVal`, `gamma_grid.maxVal` as arbitrary numbers. In this case, the value
`Gamma` is taken for gamma.
And, finally, if the optimization in a parameter is required but the corresponding grid is
unknown, you may call the function SVM::getDefaultGrid. To generate a grid, for example, for
gamma, call `SVM::getDefaultGrid(SVM::GAMMA)`.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.
*/
virtual bool trainAuto( const Ptr<TrainData>& data, int kFold = 10,
ParamGrid Cgrid = getDefaultGrid(C),
ParamGrid gammaGrid = getDefaultGrid(GAMMA),
ParamGrid pGrid = getDefaultGrid(P),
ParamGrid nuGrid = getDefaultGrid(NU),
ParamGrid coeffGrid = getDefaultGrid(COEF),
ParamGrid degreeGrid = getDefaultGrid(DEGREE),
bool balanced=false) = 0;
/** @brief Trains an %SVM with optimal parameters
@param samples training samples
@param layout See ml::SampleTypes.
@param responses vector of responses associated with the training samples.
@param kFold Cross-validation parameter. The training set is divided into kFold subsets. One
subset is used to test the model, the others form the train set. So, the %SVM algorithm is
@param Cgrid grid for C
@param gammaGrid grid for gamma
@param pGrid grid for p
@param nuGrid grid for nu
@param coeffGrid grid for coeff
@param degreeGrid grid for degree
@param balanced If true and the problem is 2-class classification then the method creates more
balanced cross-validation subsets that is proportions between classes in subsets are close
to such proportion in the whole train dataset.
The method trains the %SVM model automatically by choosing the optimal parameters C, gamma, p,
nu, coef0, degree. Parameters are considered optimal when the cross-validation
estimate of the test set error is minimal.
This function only makes use of SVM::getDefaultGrid for parameter optimization and thus only
offers rudimentary parameter options.
This function works for the classification (SVM::C_SVC or SVM::NU_SVC) as well as for the
regression (SVM::EPS_SVR or SVM::NU_SVR). If it is SVM::ONE_CLASS, no optimization is made and
the usual %SVM with parameters specified in params is executed.
*/
CV_WRAP virtual bool trainAuto(InputArray samples,
int layout,
InputArray responses,
int kFold = 10,
Ptr<ParamGrid> Cgrid = SVM::getDefaultGridPtr(SVM::C),
Ptr<ParamGrid> gammaGrid = SVM::getDefaultGridPtr(SVM::GAMMA),
Ptr<ParamGrid> pGrid = SVM::getDefaultGridPtr(SVM::P),
Ptr<ParamGrid> nuGrid = SVM::getDefaultGridPtr(SVM::NU),
Ptr<ParamGrid> coeffGrid = SVM::getDefaultGridPtr(SVM::COEF),
Ptr<ParamGrid> degreeGrid = SVM::getDefaultGridPtr(SVM::DEGREE),
bool balanced=false) = 0;
/** @brief Retrieves all the support vectors
The method returns all the support vectors as a floating-point matrix, where support vectors are
stored as matrix rows.
*/
CV_WRAP virtual Mat getSupportVectors() const = 0;
/** @brief Retrieves all the uncompressed support vectors of a linear %SVM
The method returns all the uncompressed support vectors of a linear %SVM that the compressed
support vector, used for prediction, was derived from. They are returned in a floating-point
matrix, where the support vectors are stored as matrix rows.
*/
CV_WRAP virtual Mat getUncompressedSupportVectors() const = 0;
/** @brief Retrieves the decision function
@param i the index of the decision function. If the problem solved is regression, 1-class or
2-class classification, then there will be just one decision function and the index should
always be 0. Otherwise, in the case of N-class classification, there will be \f$N(N-1)/2\f$
decision functions.
@param alpha the optional output vector for weights, corresponding to different support vectors.
In the case of linear %SVM all the alpha's will be 1's.
@param svidx the optional output vector of indices of support vectors within the matrix of
support vectors (which can be retrieved by SVM::getSupportVectors). In the case of linear
%SVM each decision function consists of a single "compressed" support vector.
The method returns rho parameter of the decision function, a scalar subtracted from the weighted
sum of kernel responses.
*/
CV_WRAP virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0;
/** @brief Generates a grid for %SVM parameters.
@param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
generated for the parameter with this ID.
The function generates a grid for the specified parameter of the %SVM algorithm. The grid may be
passed to the function SVM::trainAuto.
*/
static ParamGrid getDefaultGrid( int param_id );
/** @brief Generates a grid for %SVM parameters.
@param param_id %SVM parameters IDs that must be one of the SVM::ParamTypes. The grid is
generated for the parameter with this ID.
The function generates a grid pointer for the specified parameter of the %SVM algorithm.
The grid may be passed to the function SVM::trainAuto.
*/
CV_WRAP static Ptr<ParamGrid> getDefaultGridPtr( int param_id );
/** Creates empty model.
Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
find the best parameters for your problem, it can be done with SVM::trainAuto. */
CV_WRAP static Ptr<SVM> create();
/** @brief Loads and creates a serialized svm from a file
*
* Use SVM::save to serialize and store an SVM to disk.
* Load the SVM from this file again, by calling this function with the path to the file.
*
* @param filepath path to serialized svm
*/
CV_WRAP static Ptr<SVM> load(const String& filepath);
};
/****************************************************************************************\
* Expectation - Maximization *
\****************************************************************************************/
/** @brief The class implements the Expectation Maximization algorithm.
@sa @ref ml_intro_em
*/
class CV_EXPORTS_W EM : public StatModel
{
public:
//! Type of covariation matrices
enum Types {
/** A scaled identity matrix \f$\mu_k * I\f$. There is the only
parameter \f$\mu_k\f$ to be estimated for each matrix. The option may be used in special cases,
when the constraint is relevant, or as a first step in the optimization (for example in case
when the data is preprocessed with PCA). The results of such preliminary estimation may be
passed again to the optimization procedure, this time with
covMatType=EM::COV_MAT_DIAGONAL. */
COV_MAT_SPHERICAL=0,
/** A diagonal matrix with positive diagonal elements. The number of
free parameters is d for each matrix. This is most commonly used option yielding good
estimation results. */
COV_MAT_DIAGONAL=1,
/** A symmetric positively defined matrix. The number of free
parameters in each matrix is about \f$d^2/2\f$. It is not recommended to use this option, unless
there is pretty accurate initial estimation of the parameters and/or a huge number of
training samples. */
COV_MAT_GENERIC=2,
COV_MAT_DEFAULT=COV_MAT_DIAGONAL
};
//! Default parameters
enum {DEFAULT_NCLUSTERS=5, DEFAULT_MAX_ITERS=100};
//! The initial step
enum {START_E_STEP=1, START_M_STEP=2, START_AUTO_STEP=0};
/** The number of mixture components in the Gaussian mixture model.
Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of %EM implementation could
determine the optimal number of mixtures within a specified value range, but that is not the
case in ML yet. */
/** @see setClustersNumber */
CV_WRAP virtual int getClustersNumber() const = 0;
/** @copybrief getClustersNumber @see getClustersNumber */
CV_WRAP virtual void setClustersNumber(int val) = 0;
/** Constraint on covariance matrices which defines type of matrices.
See EM::Types. */
/** @see setCovarianceMatrixType */
CV_WRAP virtual int getCovarianceMatrixType() const = 0;
/** @copybrief getCovarianceMatrixType @see getCovarianceMatrixType */
CV_WRAP virtual void setCovarianceMatrixType(int val) = 0;
/** The termination criteria of the %EM algorithm.
The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of
M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default
maximum number of iterations is EM::DEFAULT_MAX_ITERS=100. */
/** @see setTermCriteria */
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
/** @copybrief getTermCriteria @see getTermCriteria */
CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
/** @brief Returns weights of the mixtures
Returns vector with the number of elements equal to the number of mixtures.
*/
CV_WRAP virtual Mat getWeights() const = 0;
/** @brief Returns the cluster centers (means of the Gaussian mixture)
Returns matrix with the number of rows equal to the number of mixtures and number of columns
equal to the space dimensionality.
*/
CV_WRAP virtual Mat getMeans() const = 0;
/** @brief Returns covariation matrices
Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
each matrix is a square floating-point matrix NxN, where N is the space dimensionality.
*/
CV_WRAP virtual void getCovs(CV_OUT std::vector<Mat>& covs) const = 0;
/** @brief Returns posterior probabilities for the provided samples
@param samples The input samples, floating-point matrix
@param results The optional output \f$ nSamples \times nClusters\f$ matrix of results. It contains
posterior probabilities for each sample from the input
@param flags This parameter will be ignored
*/
CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const CV_OVERRIDE = 0;
/** @brief Returns a likelihood logarithm value and an index of the most probable mixture component
for the given sample.
@param sample A sample for classification. It should be a one-channel matrix of
\f$1 \times dims\f$ or \f$dims \times 1\f$ size.
@param probs Optional output matrix that contains posterior probabilities of each component
given the sample. It has \f$1 \times nclusters\f$ size and CV_64FC1 type.
The method returns a two-element double vector. Zero element is a likelihood logarithm value for
the sample. First element is an index of the most probable mixture component for the given
sample.
*/
CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0;
/** @brief Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. Initial values of the model parameters will be
estimated by the k-means algorithm.
Unlike many of the ML models, %EM is an unsupervised learning algorithm and it does not take
responses (class labels or function values) as input. Instead, it computes the *Maximum
Likelihood Estimate* of the Gaussian mixture parameters from an input sample set, stores all the
parameters inside the structure: \f$p_{i,k}\f$ in probs, \f$a_k\f$ in means , \f$S_k\f$ in
covs[k], \f$\pi_k\f$ in weights , and optionally computes the output "class label" for each
sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most
probable mixture component for each sample).
The trained model can be used further for prediction, just like any other classifier. The
trained model is similar to the NormalBayesClassifier.
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
@param labels The optional output "class label" for each sample:
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
CV_64FC1 type.
*/
CV_WRAP virtual bool trainEM(InputArray samples,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray()) = 0;
/** @brief Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Expectation step. You need to provide initial means \f$a_k\f$ of
mixture components. Optionally you can pass initial weights \f$\pi_k\f$ and covariance matrices
\f$S_k\f$ of mixture components.
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.
@param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of
\f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be
converted to the inner matrix of such type for the further computing.
@param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of
covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices
do not have CV_64F type they will be converted to the inner matrices of such type for the
further computing.
@param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel
floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
@param labels The optional output "class label" for each sample:
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
CV_64FC1 type.
*/
CV_WRAP virtual bool trainE(InputArray samples, InputArray means0,
InputArray covs0=noArray(),
InputArray weights0=noArray(),
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray()) = 0;
/** @brief Estimate the Gaussian mixture parameters from a samples set.
This variation starts with Maximization step. You need to provide initial probabilities
\f$p_{i,k}\f$ to use this option.
@param samples Samples from which the Gaussian mixture model will be estimated. It should be a
one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type
it will be converted to the inner matrix of such type for the further computing.
@param probs0 the probabilities
@param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
@param labels The optional output "class label" for each sample:
\f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
@param probs The optional output matrix that contains posterior probabilities of each Gaussian
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
CV_64FC1 type.
*/
CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0,
OutputArray logLikelihoods=noArray(),
OutputArray labels=noArray(),
OutputArray probs=noArray()) = 0;
/** Creates empty %EM model.
The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
can use one of the EM::train\* methods or load it from file using Algorithm::load\<EM\>(filename).
*/
CV_WRAP static Ptr<EM> create();
/** @brief Loads and creates a serialized EM from a file
*
* Use EM::save to serialize and store an EM to disk.
* Load the EM from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized EM
* @param nodeName name of node containing the classifier
*/
CV_WRAP static Ptr<EM> load(const String& filepath , const String& nodeName = String());
};
/****************************************************************************************\
* Decision Tree *
\****************************************************************************************/
/** @brief The class represents a single decision tree or a collection of decision trees.
The current public interface of the class allows user to train only a single decision tree, however
the class is capable of storing multiple decision trees and using them for prediction (by summing
responses or using a voting schemes), and the derived from DTrees classes (such as RTrees and Boost)
use this capability to implement decision tree ensembles.
@sa @ref ml_intro_trees
*/
class CV_EXPORTS_W DTrees : public StatModel
{
public:
/** Predict options */
enum Flags { PREDICT_AUTO=0, PREDICT_SUM=(1<<8), PREDICT_MAX_VOTE=(2<<8), PREDICT_MASK=(3<<8) };
/** Cluster possible values of a categorical variable into K\<=maxCategories clusters to
find a suboptimal split.
If a discrete variable, on which the training procedure tries to make a split, takes more than
maxCategories values, the precise best subset estimation may take a very long time because the
algorithm is exponential. Instead, many decision trees engines (including our implementation)
try to find sub-optimal split in this case by clustering all the samples into maxCategories
clusters that is some categories are merged together. The clustering is applied only in n \>
2-class classification problems for categorical variables with N \> max_categories possible
values. In case of regression and 2-class classification the optimal split can be found
efficiently without employing clustering, thus the parameter is not used in these cases.
Default value is 10.*/
/** @see setMaxCategories */
CV_WRAP virtual int getMaxCategories() const = 0;
/** @copybrief getMaxCategories @see getMaxCategories */
CV_WRAP virtual void setMaxCategories(int val) = 0;
/** The maximum possible depth of the tree.
That is the training algorithms attempts to split a node while its depth is less than maxDepth.
The root node has zero depth. The actual depth may be smaller if the other termination criteria
are met (see the outline of the training procedure @ref ml_intro_trees "here"), and/or if the
tree is pruned. Default value is INT_MAX.*/
/** @see setMaxDepth */
CV_WRAP virtual int getMaxDepth() const = 0;
/** @copybrief getMaxDepth @see getMaxDepth */
CV_WRAP virtual void setMaxDepth(int val) = 0;
/** If the number of samples in a node is less than this parameter then the node will not be split.
Default value is 10.*/
/** @see setMinSampleCount */
CV_WRAP virtual int getMinSampleCount() const = 0;
/** @copybrief getMinSampleCount @see getMinSampleCount */
CV_WRAP virtual void setMinSampleCount(int val) = 0;
/** If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold
cross-validation procedure where K is equal to CVFolds.
Default value is 10.*/
/** @see setCVFolds */
CV_WRAP virtual int getCVFolds() const = 0;
/** @copybrief getCVFolds @see getCVFolds */
CV_WRAP virtual void setCVFolds(int val) = 0;
/** If true then surrogate splits will be built.
These splits allow to work with missing data and compute variable importance correctly.
Default value is false.
@note currently it's not implemented.*/
/** @see setUseSurrogates */
CV_WRAP virtual bool getUseSurrogates() const = 0;
/** @copybrief getUseSurrogates @see getUseSurrogates */
CV_WRAP virtual void setUseSurrogates(bool val) = 0;
/** If true then a pruning will be harsher.
This will make a tree more compact and more resistant to the training data noise but a bit less
accurate. Default value is true.*/
/** @see setUse1SERule */
CV_WRAP virtual bool getUse1SERule() const = 0;
/** @copybrief getUse1SERule @see getUse1SERule */
CV_WRAP virtual void setUse1SERule(bool val) = 0;
/** If true then pruned branches are physically removed from the tree.
Otherwise they are retained and it is possible to get results from the original unpruned (or
pruned less aggressively) tree. Default value is true.*/
/** @see setTruncatePrunedTree */
CV_WRAP virtual bool getTruncatePrunedTree() const = 0;
/** @copybrief getTruncatePrunedTree @see getTruncatePrunedTree */
CV_WRAP virtual void setTruncatePrunedTree(bool val) = 0;
/** Termination criteria for regression trees.
If all absolute differences between an estimated value in a node and values of train samples
in this node are less than this parameter then the node will not be split further. Default
value is 0.01f*/
/** @see setRegressionAccuracy */
CV_WRAP virtual float getRegressionAccuracy() const = 0;
/** @copybrief getRegressionAccuracy @see getRegressionAccuracy */
CV_WRAP virtual void setRegressionAccuracy(float val) = 0;
/** @brief The array of a priori class probabilities, sorted by the class label value.
The parameter can be used to tune the decision tree preferences toward a certain class. For
example, if you want to detect some rare anomaly occurrence, the training base will likely
contain much more normal cases than anomalies, so a very good classification performance
will be achieved just by considering every case as normal. To avoid this, the priors can be
specified, where the anomaly probability is artificially increased (up to 0.5 or even
greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is
adjusted properly.
You can also think about this parameter as weights of prediction categories which determine
relative weights that you give to misclassification. That is, if the weight of the first
category is 1 and the weight of the second category is 10, then each mistake in predicting
the second category is equivalent to making 10 mistakes in predicting the first category.
Default value is empty Mat.*/
/** @see setPriors */
CV_WRAP virtual cv::Mat getPriors() const = 0;
/** @copybrief getPriors @see getPriors */
CV_WRAP virtual void setPriors(const cv::Mat &val) = 0;
/** @brief The class represents a decision tree node.
*/
class CV_EXPORTS Node
{
public:
Node();
double value; //!< Value at the node: a class label in case of classification or estimated
//!< function value in case of regression.
int classIdx; //!< Class index normalized to 0..class_count-1 range and assigned to the
//!< node. It is used internally in classification trees and tree ensembles.
int parent; //!< Index of the parent node
int left; //!< Index of the left child node
int right; //!< Index of right child node
int defaultDir; //!< Default direction where to go (-1: left or +1: right). It helps in the
//!< case of missing values.
int split; //!< Index of the first split
};
/** @brief The class represents split in a decision tree.
*/
class CV_EXPORTS Split
{
public:
Split();
int varIdx; //!< Index of variable on which the split is created.
bool inversed; //!< If true, then the inverse split rule is used (i.e. left and right
//!< branches are exchanged in the rule expressions below).
float quality; //!< The split quality, a positive number. It is used to choose the best split.
int next; //!< Index of the next split in the list of splits for the node
float c; /**< The threshold value in case of split on an ordered variable.
The rule is:
@code{.none}
if var_value < c
then next_node <- left
else next_node <- right
@endcode */
int subsetOfs; /**< Offset of the bitset used by the split on a categorical variable.
The rule is:
@code{.none}
if bitset[var_value] == 1
then next_node <- left
else next_node <- right
@endcode */
};
/** @brief Returns indices of root nodes
*/
virtual const std::vector& getRoots() const = 0;
/** @brief Returns all the nodes
all the node indices are indices in the returned vector
*/
virtual const std::vector& getNodes() const = 0;
/** @brief Returns all the splits
all the split indices are indices in the returned vector
*/
virtual const std::vector& getSplits() const = 0;
/** @brief Returns all the bitsets for categorical splits
Split::subsetOfs is an offset in the returned vector
*/
virtual const std::vector& getSubsets() const = 0;
/** @brief Creates the empty model
The static method creates empty decision tree with the specified parameters. It should be then
trained using train method (see StatModel::train). Alternatively, you can load the model from
file using Algorithm::load\(filename).
*/
CV_WRAP static Ptr create();
/** @brief Loads and creates a serialized DTrees from a file
*
* Use DTree::save to serialize and store an DTree to disk.
* Load the DTree from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized DTree
* @param nodeName name of node containing the classifier
*/
CV_WRAP static Ptr load(const String& filepath , const String& nodeName = String());
};
/****************************************************************************************\
* Random Trees Classifier *
\****************************************************************************************/
/** @brief The class implements the random forest predictor.
@sa @ref ml_intro_rtrees
*/
class CV_EXPORTS_W RTrees : public DTrees
{
public:
/** If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance.
Default value is false.*/
/** @see setCalculateVarImportance */
CV_WRAP virtual bool getCalculateVarImportance() const = 0;
/** @copybrief getCalculateVarImportance @see getCalculateVarImportance */
CV_WRAP virtual void setCalculateVarImportance(bool val) = 0;
/** The size of the randomly selected subset of features at each tree node and that are used
to find the best split(s).
If you set it to 0 then the size will be set to the square root of the total number of
features. Default value is 0.*/
/** @see setActiveVarCount */
CV_WRAP virtual int getActiveVarCount() const = 0;
/** @copybrief getActiveVarCount @see getActiveVarCount */
CV_WRAP virtual void setActiveVarCount(int val) = 0;
/** The termination criteria that specifies when the training algorithm stops.
Either when the specified number of trees is trained and added to the ensemble or when
sufficient accuracy (measured as OOB error) is achieved. Typically the more trees you have the
better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes
pass a certain number of trees. Also to keep in mind, the number of tree increases the
prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS +
TermCriteria::EPS, 50, 0.1)*/
/** @see setTermCriteria */
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
/** @copybrief getTermCriteria @see getTermCriteria */
CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
/** Returns the variable importance array.
The method returns the variable importance vector, computed at the training stage when
CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is
returned.
*/
CV_WRAP virtual Mat getVarImportance() const = 0;
/** Returns the result of each individual tree in the forest.
In case the model is a regression problem, the method will return each of the trees'
results for each of the sample cases. If the model is a classifier, it will return
a Mat with samples + 1 rows, where the first row gives the class number and the
following rows return the votes each class had for each sample.
@param samples Array containing the samples for which votes will be calculated.
@param results Array where the result of the calculation will be written.
@param flags Flags for defining the type of RTrees.
*/
CV_WRAP virtual void getVotes(InputArray samples, OutputArray results, int flags) const = 0;
/** Returns the OOB error value, computed at the training stage when calcOOBError is set to true.
* If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting.
*/
#if CV_VERSION_MAJOR == 4
CV_WRAP virtual double getOOBError() const { return 0; }
#else
/*CV_WRAP*/ virtual double getOOBError() const = 0;
#endif
/** Creates the empty model.
Use StatModel::train to train the model, StatModel::train to create and train the model,
Algorithm::load to load the pre-trained model.
*/
CV_WRAP static Ptr<RTrees> create();
/** @brief Loads and creates a serialized RTree from a file
*
* Use RTree::save to serialize and store an RTree to disk.
* Load the RTree from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized RTree
* @param nodeName name of node containing the classifier
*/
CV_WRAP static Ptr<RTrees> load(const String& filepath , const String& nodeName = String());
};
/****************************************************************************************\
* Boosted tree classifier *
\****************************************************************************************/
/** @brief Boosted tree classifier derived from DTrees
@sa @ref ml_intro_boost
*/
class CV_EXPORTS_W Boost : public DTrees
{
public:
/** Type of the boosting algorithm.
See Boost::Types. Default value is Boost::REAL. */
/** @see setBoostType */
CV_WRAP virtual int getBoostType() const = 0;
/** @copybrief getBoostType @see getBoostType */
CV_WRAP virtual void setBoostType(int val) = 0;
/** The number of weak classifiers.
Default value is 100. */
/** @see setWeakCount */
CV_WRAP virtual int getWeakCount() const = 0;
/** @copybrief getWeakCount @see getWeakCount */
CV_WRAP virtual void setWeakCount(int val) = 0;
/** A threshold between 0 and 1 used to save computational time.
Samples with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next*
iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.*/
/** @see setWeightTrimRate */
CV_WRAP virtual double getWeightTrimRate() const = 0;
/** @copybrief getWeightTrimRate @see getWeightTrimRate */
CV_WRAP virtual void setWeightTrimRate(double val) = 0;
/** Boosting type.
Gentle AdaBoost and Real AdaBoost are often the preferable choices. */
enum Types {
DISCRETE=0, //!< Discrete AdaBoost.
REAL=1, //!< Real AdaBoost. It is a technique that utilizes confidence-rated predictions
//!< and works well with categorical data.
LOGIT=2, //!< LogitBoost. It can produce good regression fits.
GENTLE=3 //!< Gentle AdaBoost. It puts less weight on outlier data points and for that
//!<reason is often good with regression data.
};
/** Creates the empty model.
Use StatModel::train to train the model, Algorithm::load\<Boost\>(filename) to load the pre-trained model. */
CV_WRAP static Ptr<Boost> create();
/** @brief Loads and creates a serialized Boost from a file
*
* Use Boost::save to serialize and store an RTree to disk.
* Load the Boost from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized Boost
* @param nodeName name of node containing the classifier
*/
CV_WRAP static Ptr<Boost> load(const String& filepath , const String& nodeName = String());
};
/****************************************************************************************\
* Gradient Boosted Trees *
\****************************************************************************************/
/*class CV_EXPORTS_W GBTrees : public DTrees
{
public:
struct CV_EXPORTS_W_MAP Params : public DTrees::Params
{
CV_PROP_RW int weakCount;
CV_PROP_RW int lossFunctionType;
CV_PROP_RW float subsamplePortion;
CV_PROP_RW float shrinkage;
Params();
Params( int lossFunctionType, int weakCount, float shrinkage,
float subsamplePortion, int maxDepth, bool useSurrogates );
};
enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
virtual void setK(int k) = 0;
virtual float predictSerial( InputArray samples,
OutputArray weakResponses, int flags) const = 0;
static Ptr<GBTrees> create(const Params& p);
};*/
/****************************************************************************************\
* Artificial Neural Networks (ANN) *
\****************************************************************************************/
/// Multi-Layer Perceptrons //
/** @brief Artificial Neural Networks - Multi-Layer Perceptrons.
Unlike many other models in ML that are constructed and trained at once, in the MLP model these
steps are separated. First, a network with the specified topology is created using the non-default
constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is
trained using a set of input and output vectors. The training procedure can be repeated more than
once, that is, the weights can be adjusted based on the new training data.
Additional flags for StatModel::train are available: ANN_MLP::TrainFlags.
@sa @ref ml_intro_ann
*/
class CV_EXPORTS_W ANN_MLP : public StatModel
{
public:
/** Available training methods */
enum TrainingMethods {
BACKPROP=0, //!< The back-propagation algorithm.
RPROP = 1, //!< The RPROP algorithm. See @cite RPROP93 for details.
ANNEAL = 2 //!< The simulated annealing algorithm. See @cite Kirkpatrick83 for details.
};
/** Sets training method and common parameters.
@param method Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
@param param1 passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
@param param2 passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.
*/
CV_WRAP virtual void setTrainMethod(int method, double param1 = 0, double param2 = 0) = 0;
/** Returns current training method */
CV_WRAP virtual int getTrainMethod() const = 0;
/** Initialize the activation function for each neuron.
Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
@param type The type of activation function. See ANN_MLP::ActivationFunctions.
@param param1 The first parameter of the activation function, \f$\alpha\f$. Default value is 0.
@param param2 The second parameter of the activation function, \f$\beta\f$. Default value is 0.
*/
CV_WRAP virtual void setActivationFunction(int type, double param1 = 0, double param2 = 0) = 0;
/** Integer vector specifying the number of neurons in each layer including the input and output layers.
The very first element specifies the number of elements in the input layer.
The last element - number of elements in the output layer. Default value is empty Mat.
@sa getLayerSizes */
CV_WRAP virtual void setLayerSizes(InputArray _layer_sizes) = 0;
/** Integer vector specifying the number of neurons in each layer including the input and output layers.
The very first element specifies the number of elements in the input layer.
The last element - number of elements in the output layer.
@sa setLayerSizes */
CV_WRAP virtual cv::Mat getLayerSizes() const = 0;
/** Termination criteria of the training algorithm.
You can specify the maximum number of iterations (maxCount) and/or how much the error could
change between the iterations to make the algorithm continue (epsilon). Default value is
TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01).*/
/** @see setTermCriteria */
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
/** @copybrief getTermCriteria @see getTermCriteria */
CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0;
/** BPROP: Strength of the weight gradient term.
The recommended value is about 0.1. Default value is 0.1.*/
/** @see setBackpropWeightScale */
CV_WRAP virtual double getBackpropWeightScale() const = 0;
/** @copybrief getBackpropWeightScale @see getBackpropWeightScale */
CV_WRAP virtual void setBackpropWeightScale(double val) = 0;
/** BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations).
This parameter provides some inertia to smooth the random fluctuations of the weights. It can
vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough.
Default value is 0.1.*/
/** @see setBackpropMomentumScale */
CV_WRAP virtual double getBackpropMomentumScale() const = 0;
/** @copybrief getBackpropMomentumScale @see getBackpropMomentumScale */
CV_WRAP virtual void setBackpropMomentumScale(double val) = 0;
/** RPROP: Initial value \f$\Delta_0\f$ of update-values \f$\Delta_{ij}\f$.
Default value is 0.1.*/
/** @see setRpropDW0 */
CV_WRAP virtual double getRpropDW0() const = 0;
/** @copybrief getRpropDW0 @see getRpropDW0 */
CV_WRAP virtual void setRpropDW0(double val) = 0;
/** RPROP: Increase factor \f$\eta^+\f$.
It must be \>1. Default value is 1.2.*/
/** @see setRpropDWPlus */
CV_WRAP virtual double getRpropDWPlus() const = 0;
/** @copybrief getRpropDWPlus @see getRpropDWPlus */
CV_WRAP virtual void setRpropDWPlus(double val) = 0;
/** RPROP: Decrease factor \f$\eta^-\f$.
It must be \<1. Default value is 0.5.*/
/** @see setRpropDWMinus */
CV_WRAP virtual double getRpropDWMinus() const = 0;
/** @copybrief getRpropDWMinus @see getRpropDWMinus */
CV_WRAP virtual void setRpropDWMinus(double val) = 0;
/** RPROP: Update-values lower limit \f$\Delta_{min}\f$.
It must be positive. Default value is FLT_EPSILON.*/
/** @see setRpropDWMin */
CV_WRAP virtual double getRpropDWMin() const = 0;
/** @copybrief getRpropDWMin @see getRpropDWMin */
CV_WRAP virtual void setRpropDWMin(double val) = 0;
/** RPROP: Update-values upper limit \f$\Delta_{max}\f$.
It must be \>1. Default value is 50.*/
/** @see setRpropDWMax */
CV_WRAP virtual double getRpropDWMax() const = 0;
/** @copybrief getRpropDWMax @see getRpropDWMax */
CV_WRAP virtual void setRpropDWMax(double val) = 0;
/** ANNEAL: Update initial temperature.
It must be \>=0. Default value is 10.*/
/** @see setAnnealInitialT */
CV_WRAP virtual double getAnnealInitialT() const = 0;
/** @copybrief getAnnealInitialT @see getAnnealInitialT */
CV_WRAP virtual void setAnnealInitialT(double val) = 0;
/** ANNEAL: Update final temperature.
It must be \>=0 and less than initialT. Default value is 0.1.*/
/** @see setAnnealFinalT */
CV_WRAP virtual double getAnnealFinalT() const = 0;
/** @copybrief getAnnealFinalT @see getAnnealFinalT */
CV_WRAP virtual void setAnnealFinalT(double val) = 0;
/** ANNEAL: Update cooling ratio.
It must be \>0 and less than 1. Default value is 0.95.*/
/** @see setAnnealCoolingRatio */
CV_WRAP virtual double getAnnealCoolingRatio() const = 0;
/** @copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio */
CV_WRAP virtual void setAnnealCoolingRatio(double val) = 0;
/** ANNEAL: Update iteration per step.
It must be \>0 . Default value is 10.*/
/** @see setAnnealItePerStep */
CV_WRAP virtual int getAnnealItePerStep() const = 0;
/** @copybrief getAnnealItePerStep @see getAnnealItePerStep */
CV_WRAP virtual void setAnnealItePerStep(int val) = 0;
/** @brief Set/initialize anneal RNG */
virtual void setAnnealEnergyRNG(const RNG& rng) = 0;
/** possible activation functions */
enum ActivationFunctions {
/** Identity function: \f$f(x)=x\f$ */
IDENTITY = 0,
/** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x})\f$
@note
If you are using the default sigmoid activation function with the default parameter values
fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x), so the output
will range from [-1.7159, 1.7159], instead of [0,1].*/
SIGMOID_SYM = 1,
/** Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$ */
GAUSSIAN = 2,
/** ReLU function: \f$f(x)=max(0,x)\f$ */
RELU = 3,
/** Leaky ReLU function: for x>0 \f$f(x)=x \f$ and x<=0 \f$f(x)=\alpha x \f$*/
LEAKYRELU= 4
};
/** Train options */
enum TrainFlags {
/** Update the network weights, rather than compute them from scratch. In the latter case
the weights are initialized using the Nguyen-Widrow algorithm. */
UPDATE_WEIGHTS = 1,
/** Do not normalize the input vectors. If this flag is not set, the training algorithm
normalizes each input feature independently, shifting its mean value to 0 and making the
standard deviation equal to 1. If the network is assumed to be updated frequently, the new
training data could be much different from original one. In this case, you should take care
of proper normalization. */
NO_INPUT_SCALE = 2,
/** Do not normalize the output vectors. If the flag is not set, the training algorithm
normalizes each output feature independently, by transforming it to the certain range
depending on the used activation function. */
NO_OUTPUT_SCALE = 4
};
CV_WRAP virtual Mat getWeights(int layerIdx) const = 0;
/** @brief Creates empty model
Use StatModel::train to train the model, Algorithm::load\<ANN_MLP\>(filename) to load the pre-trained model.
Note that the train method has optional flags: ANN_MLP::TrainFlags.
*/
CV_WRAP static Ptr<ANN_MLP> create();
/** @brief Loads and creates a serialized ANN from a file
*
* Use ANN::save to serialize and store an ANN to disk.
* Load the ANN from this file again, by calling this function with the path to the file.
*
* @param filepath path to serialized ANN
*/
CV_WRAP static Ptr<ANN_MLP> load(const String& filepath);
};
#ifndef DISABLE_OPENCV_3_COMPATIBILITY
typedef ANN_MLP ANN_MLP_ANNEAL;
#endif
/****************************************************************************************\
* Logistic Regression *
\****************************************************************************************/
/** @brief Implements Logistic Regression classifier.
@sa @ref ml_intro_lr
*/
class CV_EXPORTS_W LogisticRegression : public StatModel
{
public:
/** Learning rate. */
/** @see setLearningRate */
CV_WRAP virtual double getLearningRate() const = 0;
/** @copybrief getLearningRate @see getLearningRate */
CV_WRAP virtual void setLearningRate(double val) = 0;
/** Number of iterations. */
/** @see setIterations */
CV_WRAP virtual int getIterations() const = 0;
/** @copybrief getIterations @see getIterations */
CV_WRAP virtual void setIterations(int val) = 0;
/** Kind of regularization to be applied. See LogisticRegression::RegKinds. */
/** @see setRegularization */
CV_WRAP virtual int getRegularization() const = 0;
/** @copybrief getRegularization @see getRegularization */
CV_WRAP virtual void setRegularization(int val) = 0;
/** Kind of training method used. See LogisticRegression::Methods. */
/** @see setTrainMethod */
CV_WRAP virtual int getTrainMethod() const = 0;
/** @copybrief getTrainMethod @see getTrainMethod */
CV_WRAP virtual void setTrainMethod(int val) = 0;
/** Specifies the number of training samples taken in each step of Mini-Batch Gradient
Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It
has to take values less than the total number of training samples. */
/** @see setMiniBatchSize */
CV_WRAP virtual int getMiniBatchSize() const = 0;
/** @copybrief getMiniBatchSize @see getMiniBatchSize */
CV_WRAP virtual void setMiniBatchSize(int val) = 0;
/** Termination criteria of the algorithm. */
/** @see setTermCriteria */
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
/** @copybrief getTermCriteria @see getTermCriteria */
CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0;
//! Regularization kinds
enum RegKinds {
REG_DISABLE = -1, //!< Regularization disabled
REG_L1 = 0, //!< %L1 norm
REG_L2 = 1 //!< %L2 norm
};
//! Training methods
enum Methods {
BATCH = 0,
MINI_BATCH = 1 //!< Set MiniBatchSize to a positive integer when using this method.
};
/** @brief Predicts responses for input samples and returns a float type.
@param samples The input data for the prediction algorithm. Matrix [m x n], where each row
contains variables (features) of one object being classified. Should have data type CV_32F.
@param results Predicted labels as a column matrix of type CV_32S.
@param flags Not used.
*/
CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const CV_OVERRIDE = 0;
/** @brief This function returns the trained parameters arranged across rows.
For a two class classification problem, it returns a row matrix. It returns learnt parameters of
the Logistic Regression as a matrix of type CV_32F.
*/
CV_WRAP virtual Mat get_learnt_thetas() const = 0;
/** @brief Creates empty model.
Creates Logistic Regression model with parameters given.
*/
CV_WRAP static Ptr<LogisticRegression> create();
/** @brief Loads and creates a serialized LogisticRegression from a file
*
* Use LogisticRegression::save to serialize and store an LogisticRegression to disk.
* Load the LogisticRegression from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized LogisticRegression
* @param nodeName name of node containing the classifier
*/
CV_WRAP static Ptr<LogisticRegression> load(const String& filepath , const String& nodeName = String());
};
/****************************************************************************************\
* Stochastic Gradient Descent SVM Classifier *
\****************************************************************************************/
/*!
@brief Stochastic Gradient Descent SVM classifier
SVMSGD provides a fast and easy-to-use implementation of the SVM classifier using the Stochastic Gradient Descent approach,
as presented in @cite bottou2010large.
The classifier has following parameters:
- model type,
- margin type,
- margin regularization (\f$\lambda\f$),
- initial step size (\f$\gamma_0\f$),
- step decreasing power (\f$c\f$),
- and termination criteria.
The model type may have one of the following values: \ref SGD and \ref ASGD.
- \ref SGD is the classic version of SVMSGD classifier: every next step is calculated by the formula
\f[w_{t+1} = w_t - \gamma(t) \frac{dQ_i}{dw} |_{w = w_t}\f]
where
- \f$w_t\f$ is the weights vector for decision function at step \f$t\f$,
- \f$\gamma(t)\f$ is the step size of model parameters at the iteration \f$t\f$, it is decreased on each step by the formula
\f$\gamma(t) = \gamma_0 (1 + \lambda \gamma_0 t) ^ {-c}\f$
- \f$Q_i\f$ is the target functional from SVM task for sample with number \f$i\f$, this sample is chosen stochastically on each step of the algorithm.
- \ref ASGD is Average Stochastic Gradient Descent SVM Classifier. ASGD classifier averages weights vector on each step of algorithm by the formula
\f$\widehat{w}_{t+1} = \frac{t}{1+t}\widehat{w}_{t} + \frac{1}{1+t}w_{t+1}\f$
The recommended model type is ASGD (following @cite bottou2010large).
The margin type may have one of the following values: \ref SOFT_MARGIN or \ref HARD_MARGIN.
- You should use \ref HARD_MARGIN type, if you have linearly separable sets.
- You should use \ref SOFT_MARGIN type, if you have non-linearly separable sets or sets with outliers.
- In the general case (if you know nothing about linear separability of your sets), use SOFT_MARGIN.
The other parameters may be described as follows:
- Margin regularization parameter is responsible for weights decreasing at each step and for the strength of restrictions on outliers
(the less the parameter, the less probability that an outlier will be ignored).
Recommended value for SGD model is 0.0001, for ASGD model is 0.00001.
- Initial step size parameter is the initial value for the step size \f$\gamma(t)\f$.
You will have to find the best initial step for your problem.
- Step decreasing power is the power parameter for \f$\gamma(t)\f$ decreasing by the formula, mentioned above.
Recommended value for SGD model is 1, for ASGD model is 0.75.
- Termination criteria can be TermCriteria::COUNT, TermCriteria::EPS or TermCriteria::COUNT + TermCriteria::EPS.
You will have to find the best termination criteria for your problem.
Note that the parameters margin regularization, initial step size, and step decreasing power should be positive.
To use SVMSGD algorithm do as follows:
- first, create the SVMSGD object. The algorithm will set optimal parameters by default, but you can set your own parameters via functions setSvmsgdType(),
setMarginType(), setMarginRegularization(), setInitialStepSize(), and setStepDecreasingPower().
- then the SVM model can be trained using the train features and the correspondent labels by the method train().
- after that, the label of a new feature vector can be predicted using the method predict().
@code
// Create empty object
cv::Ptr<SVMSGD> svmsgd = SVMSGD::create();
// Train the Stochastic Gradient Descent SVM
svmsgd->train(trainData);
// Predict labels for the new samples
svmsgd->predict(samples, responses);
@endcode
*/
class CV_EXPORTS_W SVMSGD : public cv::ml::StatModel
{
public:
/** SVMSGD type.
ASGD is often the preferable choice. */
enum SvmsgdType
{
SGD, //!< Stochastic Gradient Descent
ASGD //!< Average Stochastic Gradient Descent
};
/** Margin type.*/
enum MarginType
{
SOFT_MARGIN, //!< General case, suits to the case of non-linearly separable sets, allows outliers.
HARD_MARGIN //!< More accurate for the case of linearly separable sets.
};
/**
* @return the weights of the trained model (decision function f(x) = weights * x + shift).
*/
CV_WRAP virtual Mat getWeights() = 0;
/**
* @return the shift of the trained model (decision function f(x) = weights * x + shift).
*/
CV_WRAP virtual float getShift() = 0;
/** @brief Creates empty model.
* Use StatModel::train to train the model. Since %SVMSGD has several parameters, you may want to
* find the best parameters for your problem or use setOptimalParameters() to set some default parameters.
*/
CV_WRAP static Ptr<SVMSGD> create();
/** @brief Loads and creates a serialized SVMSGD from a file
*
* Use SVMSGD::save to serialize and store an SVMSGD to disk.
* Load the SVMSGD from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized SVMSGD
* @param nodeName name of node containing the classifier
*/
CV_WRAP static Ptr<SVMSGD> load(const String& filepath , const String& nodeName = String());
/** @brief Function sets optimal parameters values for chosen SVM SGD model.
* @param svmsgdType is the type of SVMSGD classifier.
* @param marginType is the type of margin constraint.
*/
CV_WRAP virtual void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN) = 0;
/** @brief %Algorithm type, one of SVMSGD::SvmsgdType. */
/** @see setSvmsgdType */
CV_WRAP virtual int getSvmsgdType() const = 0;
/** @copybrief getSvmsgdType @see getSvmsgdType */
CV_WRAP virtual void setSvmsgdType(int svmsgdType) = 0;
/** @brief %Margin type, one of SVMSGD::MarginType. */
/** @see setMarginType */
CV_WRAP virtual int getMarginType() const = 0;
/** @copybrief getMarginType @see getMarginType */
CV_WRAP virtual void setMarginType(int marginType) = 0;
/** @brief Parameter marginRegularization of a %SVMSGD optimization problem. */
/** @see setMarginRegularization */
CV_WRAP virtual float getMarginRegularization() const = 0;
/** @copybrief getMarginRegularization @see getMarginRegularization */
CV_WRAP virtual void setMarginRegularization(float marginRegularization) = 0;
/** @brief Parameter initialStepSize of a %SVMSGD optimization problem. */
/** @see setInitialStepSize */
CV_WRAP virtual float getInitialStepSize() const = 0;
/** @copybrief getInitialStepSize @see getInitialStepSize */
CV_WRAP virtual void setInitialStepSize(float InitialStepSize) = 0;
/** @brief Parameter stepDecreasingPower of a %SVMSGD optimization problem. */
/** @see setStepDecreasingPower */
CV_WRAP virtual float getStepDecreasingPower() const = 0;
/** @copybrief getStepDecreasingPower @see getStepDecreasingPower */
CV_WRAP virtual void setStepDecreasingPower(float stepDecreasingPower) = 0;
/** @brief Termination criteria of the training algorithm.
You can specify the maximum number of iterations (maxCount) and/or how much the error could
change between the iterations to make the algorithm continue (epsilon).*/
/** @see setTermCriteria */
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
/** @copybrief getTermCriteria @see getTermCriteria */
CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
};
/****************************************************************************************\
* Auxiliary functions declarations *
\****************************************************************************************/
/** @brief Generates _sample_ from multivariate normal distribution
@param mean an average row vector
@param cov symmetric covariation matrix
@param nsamples returned samples count
@param samples returned samples array
*/
CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples);
/** @brief Creates test set */
CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
OutputArray samples, OutputArray responses);
/****************************************************************************************\
* Simulated annealing solver *
\****************************************************************************************/
#ifdef CV_DOXYGEN
/** @brief This class declares example interface for system state used in simulated annealing optimization algorithm.
@note This class is not defined in C++ code and can't be use directly - you need your own implementation with the same methods.
*/
struct SimulatedAnnealingSolverSystem
{
/** Give energy value for a state of system.*/
double energy() const;
/** Function which change the state of system (random perturbation).*/
void changeState();
/** Function to reverse to the previous state. Can be called once only after changeState(). */
void reverseState();
};
#endif // CV_DOXYGEN
/** @brief The class implements simulated annealing for optimization.
@cite Kirkpatrick83 for details
@param solverSystem optimization system (see SimulatedAnnealingSolverSystem)
@param initialTemperature initial temperature
@param finalTemperature final temperature
@param coolingRatio temperature step multiplies
@param iterationsPerStep number of iterations per temperature changing step
@param lastTemperature optional output for last used temperature
@param rngEnergy specify custom random numbers generator (cv::theRNG() by default)
*/
template<class SimulatedAnnealingSolverSystem>
int simulatedAnnealingSolver(SimulatedAnnealingSolverSystem& solverSystem,
double initialTemperature, double finalTemperature, double coolingRatio,
size_t iterationsPerStep,
CV_OUT double* lastTemperature = NULL,
cv::RNG& rngEnergy = cv::theRNG()
);
//! @} ml
}
}
#include /** Creates the empty model.
Use StatModel::train to train the model, Algorithm::load
CV_WRAP static Ptr create();
/** @brief Loads and creates a serialized Boost from a file
*
* Use Boost::save to serialize and store an RTree to disk.
* Load the Boost from this file again, by calling this function with the path to the file.
* Optionally specify the node for the file containing the classifier
*
* @param filepath path to serialized Boost
* @param nodeName name of node containing the classifier
*/
/**如果为true，那么将计算变量重要性，然后可以通过RTrees:：getVarImportance检索它。
默认值为false*/
/**@请参阅SetCalculateVarimImportance*/
CV\u WRAP virtual bool getCalculateVarImportance（）const=0；
/**@copybrief getCalculateVarImportance@请参阅getCalculateVarImportance*/
CV\u WRAP virtual void setcalculatevalimportance（bool val）=0；
/**每个树节点上随机选择的特征子集的大小，以及所使用的特征子集的大小
找到最佳分割。
如果将其设置为0，则大小将设置为
特征。默认值为0*/
/**@见setActiveVarCount*/
CV\u WRAP virtual int getActiveVarCount（）const=0；
/**@copybrief getActiveVarCount@请参阅getActiveVarCount*/
CV\u WRAP virtual void setActiveVarCount（int val）=0；
/**指定训练算法何时停止的终止条件。
当指定数量的树被训练并添加到集合中时，或者
达到足够的精度（作为OOB误差测量）。通常你的树越多
准确度更高。然而，精度的提高通常是逐渐减小和渐近线的
经过一定数量的树。还要记住的是，树的数量增加了
预测时间呈线性关系。默认值为TermCriteria（TermCriteria:：MAX\）+
术语标准：：每股收益，50，0.1）*/
/**@见标准*/
CV\u WRAP virtual TermCriteria getTermCriteria（）const=0；
/**@copybrief getTermCriteria@请参阅getTermCriteria*/
CV\u WRAP virtual void setTermCriteria（const TermCriteria&val）=0；
/**返回变量重要性数组。
该方法返回在训练阶段计算的变量重要性向量
CalculateValimportance设置为true。如果此标志设置为false，则空矩阵为
返回。